If a 'Yes' response is supplied by the participant, the volume in the next trial is reduced by 1 dB. And if a 'No' response is supplied, the volume in the next trial is increased by 1 dB. An experiment will include 30 trials per frequency. A sample of a table that includes the required outputs from an auditory staircase experiment. Note that data reported are for a single subject labeled Subject 1 and for a single frequency Hz. The table includes three columns: Sample results from a single participant and with a single tone.
The graph plots the volume of the tone played, in dB, as a function of the trial number for each of the 30 trials.
The main pattern is that the participant cannot hear any tone in the first few trials, producing a series of 'No' responses and prompting volume increases until the auditory threshold is reached. At that point, the participant moves back and forth between 'No' and 'Yes' responses allowing the researcher to identify the place at which sounds first become detectable.
Just like actual levels, perceptual ones can be measured: This dynamic adjustment is the concept behind the staircase procedure, where the minimum noticed intensity can be reliably determined by stepping up or down the amount of stimulation. This video demonstrates how to design and implement the staircase procedure, specifically to measure auditory thresholds—the minimal volume necessary for a tone to be perceived.
In this experiment, tones are presented through headphones at six different frequencies or pitches: Given that our thresholds are not the same across all frequencies, six blocks are used to test each one independently. In each block, the frequency is briefly presented for ms at volumes ranging from 2—40 dB.
The first tone is played at the lowest volume of 2 dB, a level that the participant should not perceive. On the other hand, if it is noticeable, the volume is decreased by one. This procedure is repeated for 30 trials—resulting in staircase-like changes in volume. This information is then combined with the volume intensity data to determine the perceptual volume threshold at each frequency. To begin the experiment, greet the participant in the lab and have them sit comfortably in front of the computer. Explain the task instructions: In each trial, the computer will play a tone through the headphones, after which you will be prompted to press the 'Y' key if you heard the tone or 'N' if you did not.
Allow the participant to put on the headphones, start the trials associated with the 1 kHz tone, and then leave the room. After the first block of six frequencies is completed, return to the room and ask the participant to remove the headphones. Answer any questions they may have and give them a 2-min break. When time is up, have the participant put the headphones back on to begin the trials related to the next tone. Repeat the steps until all six tones have been tested.
Following verification, graph the volume played on each trial of every block as shown here for 1 kHz. When the auditory threshold was reached, notice that the participant moved back and forth between 'No' and 'Yes' responses, which allows for the identification of what sounds first became detectable. The central tendency of this narrow range is a measure of the threshold. To calculate the volume threshold at each tone, average the last 10 trials of every block and graph the results.
Observe how this tended to increase as the frequency increased; in other words, low-pitched tones were easier to hear than high-pitched ones, which is due to the vibration properties of the filaments and bones of the ear. The staircase procedure has been used by researchers to examine how hearing thresholds change as humans age. In general, they found that volume thresholds increased as people get older. That is, for individuals aged 60, a high-frequency sound needed to be four times as loud as it would to be audible by those who are 20 years old.
Using similar methods, researchers also compared volume thresholds of people with normal hearing to those with impairments to identify the nature of the deficits. Specific frequencies were affected, such as at 4 and 5 kHz, whereas others were normal, suggesting that disease or damage is the cause, not aging. In addition, the approach can be used to assess the consequences of various types of experiences on the auditory system.
For example, studies have used a threshold approach to evaluate the effects of hearing loud heavy-metal music during a concert.
When researchers tested people just before attending a concert, and a half an hour after, they found that heavy metal increased the volume threshold for sounds. Thus, rock music can make you hard of hearing! Now you should have a good understanding of how to design a perceptual threshold task and run the experiment, as well as analyze and assess the results.
The aim of the staircase procedure is to bring the participant to a volume at which they can just barely hear a tone. This is achieved by prompting a series of 'No' responses in the first few trials. Once a 'Yes' response is produced, the goal is to keep the volume played close to the one that elicited the first 'Yes'.
This is done by lowering the volume whenever a 'Yes' response is given. This produces a pattern in which the volume rises steadily in the first few trials, and then plateaus, remaining in a narrow range until the end of the experiment, as seen in Figure 3. In Figure 3, it is clear that the threshold is reached at around 6 dB. A common way to calculate the threshold is to compute the average of the volumes played during the last 10 trials of the experiments. In the case of Figure 3, that average works out to 6. With results obtained for six tones of different frequencies, one can see that perceptibility thresholds vary by frequency what is often called pitch.
In general, each subject took part in one to two sessions per week. Our protocol specified that OCT measurements were performed on the subjects only for clinical reasons, and as a result OCT data were collected at irregular intervals. Figure 1 shows a fundus image of the intraocular electrode array in S3 viewed through a dilated pupil.
The array was attached to the retina using a retinal tack. The distant return electrode was placed on the electronics case, and a wire SC cable connected the intraocular electrode array to the extraocular unit through the sclera.
The first systematic studies to determine sensory thresholds were conducted by Ernst Heinrich Weber , a physiologist and pioneer of experimental psychology at the Leipzig University. It was also possible that long-term stimulation led to electrode corrosion. In each subject, we calculated the best-fitting linear regression over time across all electrodes. Throughout testing, subjects were blind to amplitude of stimulus current. Rock music can make you hard of hearing! How bright does a light need to be for a person to be able to detect it? Arch Phys Med Rehabil.
OCT imaging of the array. Red and orange arrows indicate how electrode distance and retinal thickness were measured. The extraocular component of the implant, which converts a radio frequency signal into electrical stimulation patterns, was surgically implanted in the temporal bone, similar to a cochlear implant.
The desired pulse pattern was generated on a computer and sent to a custom-built video processing unit Second Sight Medical Products, Inc. A reverse telemetry function in the implant allowed direct measurement of the impedance of each electrode. Figure 2a shows a fundus image in S6 of an intraocular electrode array with the OCT imaging light source visible a single line.
The arrow represents the direction of the imaging. Figure 2b shows the image of the cross-section of the retina that lay under the OCT imaging light source in Figure 2a. Broad shadows are cast by the electrodes, and narrow shadows are due to the passage of the imaging light source across the edge of the electrode as is the case in electrode 3 in this example or are cast by individual wires within the array note that wires also pass above individual electrodes. Corresponding electrodes are labeled across Figures 2a and 2b.
The small deviation between the position of the scan line in the fundus image and the actual OCT image position was due to the occurrence of slight eye movements in the very short time interval that separated acquisition of the two images. Because our subjects have nystagmus uncontrolled eye movement , obtaining clear OCT images was time consuming and physically demanding.
Consequently, OCT measurements could not be gathered for every electrode for every subject. As shown in Figure 2b , we measured the distance from the top of each electrode to the internal limiting membrane of the retina red arrows. The thickness of the retina was therefore defined as the distance from the inner surface of the retinal pigment epithelium to the internal limiting membrane Fig. As can be seen in Figure 2 , it was not always easy to determine the exact position of the top of the electrode, the surface of the internal limiting membrane, or the inner surface of the retinal pigment epithelium.
We cross-validated their judgments by having both experimenters analyze the same subset of data. If the two experimenters' judgments correlated perfectly, then their judgments would fall along a line of slope equalling 1. The best-fitting regression for estimates of electrode distance had a slope of 1. A Monte-Carlo procedure in which each judgment was randomly assigned to an experimenter was used to assess whether the best-fitting regression slope for these data differed significantly from 1.
The best-fitting regression for estimates of retinal thickness had a slope of 0. The high consistency across experimenters demonstrates that trained observers could make consistent judgments about electrode distance and retinal thickness on the basis of our OCT images. Impedance measurements were taken at the beginning and end of each stimulating session. Cathodic and anodic pulses were separated by a 0.
All pulse waveforms were biphasic and charge balanced.
Until September , a relatively crude technique was used to measure the perceptual threshold. In each trial, subjects were given verbal feedback that a pulse was about to be presented, the subject was stimulated and was then asked to indicate verbally whether the stimulation had caused a visible percept. Thresholds were calculated by pooling data across sessions and the probability of the subject's reporting a percept was plotted as a function of stimulation intensity.
The amplitude of the test pulse was varied by using a three-up—one-down staircase. If the subject responded correctly three times in a row, the task was made more difficult by decreasing the current amplitude, if the subject answered incorrectly on any trial, the task was made easier by increasing the current amplitude.
Each threshold was based on approximately to trials, generally 50 trials are adequate to estimate threshold with reasonable accuracy. After November , this procedure was automated, and subjects indicated whether they saw a stimulus on each trial via a computer key press. We again confirmed that this experimental modification did not significantly affect threshold estimates or the probability of false positive responses. Changes in a given measurement over time e. Statistically significant changes over time are reported both for each subject and across all six subjects.
To quantify the relationship between threshold, impedance, electrode distance, and retinal thickness, we had to find corresponding measurements over time across these different measures. We partitioned our data into day time periods. For example, a given data point comparing impedance and threshold values might represent the average across several impedance measurements and several threshold measurements, both collected within the same day time period e.
Electrode distance from the retina and retinal thickness measurements were taken less frequently, but the same approach was still applied: Electrode distance from the retina and retinal thickness estimates were compared to impedance or threshold measurements taken within the same day time window as the OCT measurement.
Phosphene appearance near threshold was typically white or yellow, and the phosphenes were reported to be round or oval. Occasionally, subjects would report seeing a dark spot. If a dark phosphene was seen in response to stimulation with a given electrode, an increase in the stimulation current generally resulted in the subject's seeing a light spot in the same location. As stimulation current increased, the brightness of the percept then tended to increase monotonically.
In all subjects, phosphenes at threshold were not uncomfortable. Mean thresholds over the entire period during which we collected data are shown for each electrode and subject in Figure 3. Mean thresholds decreased dramatically across subject implantations. Note that subject S1 was much older than the other subjects, had no light perception NLP vision for much longer than any of the other subjects, and may have had confounding retinal or optic nerve damage.
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These differences in threshold did not change systematically with the patient's age; level of preoperative vision, as recorded clinically or measured using the dark-adapted light flash 29 ; preoperative electrically evoked responses measured with a Burian-Allen corneal electrode 29 ; or implant technology. However, thresholds appeared to decrease systematically across successive surgeries, perhaps because the positioning of the electrode array was successively closer to the retinal surface on each surgery.
Mean thresholds across the entire period of implantation of all 16 electrodes in each subject. In each subject, electrodes were ordered from most to least sensitive along the x -axis. Threshold current is shown along the y -axis. Note the dramatic change of scale along the y -axis across subjects. These thresholds are well below charge density limits of 0. It should also be noted that these thresholds are for a single pulse, whereas functional electrical stimulation is likely to be mediated by pulse trains, which generally require lower stimulation thresholds.
We compared mean thresholds across these two electrode sizes for these three subjects. Other data shown in this article described later suggest that the distance of electrodes from the retinal surface has a dramatic effect on threshold. However, the checkerboard arrangement used in these three subjects provided a means of crudely factoring out the effects of electrode distance from the retinal surface. The arrays were fairly rigid, and therefore nearby electrodes tended to have similar distances from the retinal surface.
This checkerboard pattern therefore tended to minimize differences in distance from the retinal surface across the two electrodes sizes in a given subject. Thresholds as a function of electrode diameter. Data from electrodes in subjects S4 to S6, implanted in a checkerboard configuration, are shown. Symbols connected by lines: In many cases, error bars are smaller than the symbols.
Individual electrodes are shown with small symbols. Replotted from Sekirnjak et al. As described earlier, the log threshold current necessary to elicit spikes within in vitro retinal ganglion cells correlates linearly with log electrode area. It is possible that a wider range of electrode sizes in our subjects with implants would make threshold differences as a function of electrode size more apparent.
It is also possible, given the large electrode sizes used in this experiment, that current density had a nonuniform distribution and was concentrated at the electrode edges of the electrodes see the Discussion section. As described in the introduction and illustrated in Figure 4b , previous acute and chronic human studies show remarkable variability in threshold Rizzo JF III et al. These latter subjects demonstrate that the current levels required to elicit percepts in humans can be of the same order of magnitude as the current levels required within in vitro experiments in which similar electrode sizes are used.
Subject thresholds tended to increase after surgery as shown for two of the six subjects in Figure 5 top row. As described in the Discussion section, a possible explanation for these increases is that the electrode array may have tended to lift off the retina after surgery. While reasonably well fit by a linear regression, each subject showed an individual pattern of threshold instability over time.
Thresholds, impedance, distance between the electrodes and the retinal surface, and retinal thickness as a function of time in two subjects. Each color represents a different electrode. The x -axis represents days after surgery. In each subject, we calculated the best-fitting linear regression over time across all electrodes. The y -axis represents threshold current for a 0. Note that both x- and y -axes differ across subjects. For this subject, we calculated separate linear regressions for each array attachment. Thresholds were lower and impedances higher after reattachment. For both subjects the mean best-fitting linear regression line across all electrodes is shown black dotted line.
The mean best-fitting linear regression line across all electrodes is shown black dotted line. For each subject, we calculated absolute differences in threshold as a function of the time between the two measurements for each electrode, using every possible time-pair for S2, each implantation was treated separately. These absolute differences in threshold as a function of time were then fit by linear regression. We found, for every subject, that the slopes of these regressions across the 16 electrodes were significantly greater than 0 one-tailed t -test, S1, S2, S4, S5, and S6: Mean slopes of the linear regression varied between 0.
Thus, much of the variation in threshold across repeated measurements was due to changes in threshold over time as opposed to measurement error. Impedance as a function of electrode diameter. Large symbols connected by lines: We found that impedance varied across measurements over time, as shown for two subjects in the second row of Figure 5. On the whole, subject impedances tended to decrease over time after surgery. Although their impedances were reasonably well fit by linear regression, each subject showed an individual pattern of impedance instability over time.
As described in the Discussion section, we believe that these changes in impedance may be driven by changes in the distance of the electrode array from the retinal surface. It should be noted that we also saw an initial instability in impedances in the first weeks after implantation and stimulation the time scale of years in Fig.
In patients with cochlear implants, changes in impedance in the first weeks after implantation are generally attributed to changes in the tissue surrounding the electrode and to electrochemical changes at the electrode interface. The two bottom rows of Figure 5 show measured distances of electrodes from the retinal surface and measured retinal thicknesses. Occasionally, multiple OCT images of the same electrode were taken on the same day.
In these cases, measurements of electrode distance and retinal thickness for that electrode were averaged, and standard errors were calculated. The aim of this study was to develop a quantitative sensory test QST that could be used for assessing the level and the density degree of impairment of spinal cord injury SCI and for monitoring neurological changes in patients with SCI.
Electrical perceptual threshold EPT was recorded as the lowest ascending stimulus intensity out of three tests at which the subject reported sensation. In the control group, EPT depended on the dermatome tested and was lowest for T1 1. There was strong correlation between corresponding right and left dermatomes and between repeated assessments.